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Aspects of linguistic ageing in literary authors across time

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Języki publikacji
EN
Abstrakty
EN
This work offers an investigation into linguistic changes in a corpus of literary authors hypothesized to be attributable to the effects of ageing. In part, the analysis replicates an earlier study into these effects, but adds to it by explicitly analyzing and modelling competing factors, specifically the influence of background language change. Our results suggest that it is likely that this underlying change in language usage is the primary force for the change observed in the linguistic variables that was previously attributed to linguistic ageing. However, our results are tentative insofar as we do not examine non-linear models in general, or other variables influenced by ageing, or non-professional writers who may be more susceptible to these observed shifts in general language than was observable for the literary authors.
Rocznik
Strony
195--223
Opis fizyczny
Bibliogr. 21 poz., tab., rys., wykr.
Twórcy
  • School of Computer Science and Statistics, O’Reilly Institute, Trinity College Dublin, Ireland
autor
  • School of Computer Science and Statistics, O’Reilly Institute, Trinity College Dublin, Ireland
  • School of Computer Science and Statistics, O’Reilly Institute, Trinity College Dublin, Ireland
Bibliografia
  • 1. Joseph Warren BEACH (1918), The method of Henry James, Yale University Press.
  • 2. Helen BIRD, Matthew A. LAMBON RALPH, Karalyn PATTERSON ,and John R.HODGES (2000), The rise and all of frequency and image ability: noun and verb production in semantic dementia, Brain and Language,73(1):17-49, https://www.sciencedirect.com/science/article/pii/S0093934X00922934.
  • 3. Deborah M. BURKE and Meredith A. SHAFTO (2008), Language and ageing, Science, 346 : 373-443.
  • 4. Henry Seidel CANBY (1951), Turn west, turn east: Mark Twain and Henry James, Biblo &Tannen Publishers.
  • 5. Mark DAVIES (2012), The 400 million word Corpus of Historical American English (1810–2009), in English Historical Linguistics 2010: Selected Papers from the Sixteenth International Conference on English Historical linguistics (ICeHl 16), Pécs, 23-27 August 2010, pp.231-61.
  • 6. Richard S.FORSYTH (1999), Stylo chronometry with substrings, or: a poet young and old, Literary and Linguistic Computing, 14(4) : 467-478.
  • 7. David L.HOOVER (2007), Corpus stylistics, stylometry, and the styles of Henry James, Style, 41(2) : 174-203.
  • 8. Kurt HORNIK (2015), open NLP : Apache Open \NLP tools interface, https://CRAN.R-project.org/package=openNLP, R package version 0.2-5.
  • 9. Kurt HORNIK (2016), NLP : natural language processing infrastructure, https://CRAN.R-project.org/package=NLP, R package version 0.1-9.
  • 10. Susan KEMPER, Lydia H. GREINER, Janet G. MARQUIS, Katherine PRENOVOST, and Tracy L. MITZNER (2001), Language decline across the life span : findings from the nun study, Psychology and Aging, 16(2) : 227-239.
  • 11. Carmen KLAUSSNER and Carl VOGEL (2018a), A diachronic corpus for literary style analysis, in Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), European Language Resources Association(ELRA).
  • 12. Carmen KLAUSSNER and Carl VOGEL (2018b), Temporal predictive regression models for linguistic style analysis, Journal of Language Modelling,6(1):175-222.
  • 13. Xuan LE, Ian LANCASHIRE, Graeme HIRST, and Regina JOKEL (2011), Longitudinal detection of dementia through lexical and syntactic changes in writing : a case study of three British novelists, Literary and Linguistic Computing,26(4):435-461.
  • 14. Erez LIEBERMAN, Jean-Baptiste MICHEL, Joe JACKSON, Tina TANG, and Martin A. NOWAK (2007), Quantifying the evolutionary dynamics of language, Nature, 449(7163):713.
  • 15. Edse lA.PENA and Elizabeth H. SLATE (2014), gvlma : global validation of linearmodels assumptions,http://CRAN.R-project.org/package=gvlma, R package version 1.0.0.2 (last verified : 24.08.2015).
  • 16. JamesW.PENNEBAKER andLoriD.STONE (2003),Wordsofwisdom:languageuseoverthelifespan,Journal of Personality and Social Psychology,85(2):291-231.
  • 17. Jose PINHEIRO, Douglas BATES, Saikat DEBROY, Deepayan SARKAR, and R.CORE TEAM (2013), nlme: linear and nonlinear mixed effects models, R package version 3.1-113.
  • 18. R. CORE TEAM (2014), R: a language and environment for statistical computing, R Foundation for Statistical Computing, Vienna, Austria, http://www.r-project.org.
  • 19. Sanja ŠTAJNER and Ruslan MITKOV (2011), Diachronic stylistic changes in British and American varieties of 20th-century written English language, in Proceedings of the Workshop on Language Technologies for Digital Humanities and Cultural Heritage at RANLP,pp.78-85, https://www.aclweb.org/anthology/W11-4112/.
  • 20. Constantina STAMOU (2007), Stylochronometry: stylistic development, sequence of composition, and relative dating, Literary and Linguistic Computing, 23(2): 181-199.
  • 21. W.N.VENABLES and B.D.RIPLEY (2002), Modern applied statistics with S, Springer, fourth edition, http://www.stats.ox.ac.uk/pub/MASS4
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023). (PL)
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-e72591f6-1a67-45f8-ba8d-0404e766486c
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